Journal ArticleDOI
Background learning for robust face recognition with PCA in the presence of clutter
TLDR
A new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter by learning the distribution of background patterns and it is shown how this can be done for a given test image.Abstract:
We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms.read more
Citations
More filters
Journal Article
Deep Networks for Image and Video Super-Resolution
TL;DR: This work proposes a deep architecture for single image super-resolution (SISR), which is built using efficient convolutional units the authors refer to as mixed-dense connection blocks (MDCB), which combines the strengths of both residual and dense connection strategies, while overcoming their limitations.
Journal ArticleDOI
Zero shot framework for satellite image restoration
TL;DR: In this paper , a distortion disentanglement and knowledge distillation framework for satellite image restoration is proposed to address the issue of limited training data and unsuitable when only a few images are available.
Proceedings ArticleDOI
Face Recognition Systems: Are you sure they only consider your face?
TL;DR: The impact of background on the recommended measure of similarity, Euclidean-L2, across different pictures that represent distinguishable emotions and image background is reported, finding that this impact of the background varies for different ethnic groups.
Journal Article
Image Restoration using Feature-guidance
TL;DR: A new approach suitable for handling the image-specific and spatially-varying nature of degradation in images affected by practically occurring artifacts such as blur, rain-streaks is presented.
References
More filters
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.